X-ray imaging technology provides a non-invasive technique for visualizing the internal structure of an object of interest by exposing the object to high energy electromagnetic radiation (i.e., X-rays). X-rays emitted from a radiation source interact with the object and are absorbed, scattered and/or diffracted at varying levels by the internal structures of the object. Transmitted X-ray radiation, for example, is attenuated according to the various absorption characteristics of the materials which the X-rays encounter. By measuring the attenuation of the X-ray radiation that exits the object, information related to the density distribution of the object may be obtained.
To obtain X-ray information about an object, an X-ray source and an array of detectors responsive to X-ray radiation may be arranged about the object. Each detector in the array, for example, may generate an electrical signal proportional to the intensity and/or energy of X-ray radiation impinging on a surface of the detector. The source and array may be rotated around the object in a predetermined path to obtain a number of views of the object at different angles. At each view, the detector signal generated by each detector in the array indicates the total absorption (i.e., attenuation) incurred by material substantially in a line between the X-ray source and the detector. Therefore, the array of detection signals records the projection of the object onto the detector array at a number of views of the object, and provides one method of obtaining view data of the object.
View data obtained from an X-ray scanning device may be of any form that provides transmission (attenuation), scatter and/or diffraction information as a function of view angle or orientation with respect to the object being scanned. View data may be obtained by exposing a planar cross-section of an object, referred to as a slice, to X-ray radiation. Each rotation about the object (e.g., a 180° rotation of the radiation source and detector array) provides information about the interaction of X-rays with a two-dimensional (2D) slice of the object.
Accordingly, the X-ray scanning process transforms a generally unknown material distribution of an object into view data having information about how the X-rays interacted with the unknown density. For example, the view data may indicate how the material distribution attenuated the X-rays, providing information related to the density and/or atomic number of the material distribution. FIG. 1A illustrates a diagram of the transformation operation performed by the X-ray scanning process. An object 100 having an unknown density distribution in object space is subjected to X-ray scanning. Object space refers herein to the coordinate frame of an object of interest, for example, an object undergoing an X-ray scan. A Cartesian coordinate frame (i.e., (x, y, z)) may be a convenient coordinate system for object space, however, object space may be described by any other suitable coordinate frame, such as spherical or cylindrical coordinates.
X-ray scanning process 110 generates object view data 105 in a view space coordinate frame (e.g., coordinate frame (t,θ,z)). For example, object view data 105 may include attenuation information from a plurality of detectors in an array (corresponding to the view space t-axis), at a number of orientations of the X-ray scanning device (corresponding to the view space θ-axis), over a number of cross-sections of the object (corresponding to the view space z-axis) to form three dimensional (3D) view data. The 3D view data may be considered as a series of 2D slices stacked on top of one another to form the third axis (e.g., the z-axis). Accordingly, X-ray scanning process 110 transforms a continuous density distribution in object space to discrete view data in view space.
To reconstruct the density distribution of the object from the view data, the view data may be projected back into object space. The process of transforming view data in view space into reconstructed data represented in object space is referred to as reconstruction. FIG. 1B illustrates a reconstruction process 120 that transforms view data 105 into reconstructed data 100′ (e.g., a reconstructed density image of a portion of object 100). To form reconstructed data 100′, a density value for each desired discrete location of object 100 in object space is determined based on the information available in view data 105. It should be appreciated that 2D and 3D images in an object space coordinate frame (e.g., images that generally mimic the appearance of subject matter as it is perceived by the human visual system) are reconstructed data. Many techniques have been developed for reconstruction to transform acquired view data into reconstructed data. For example, various iterative methods, Fourier analysis, back-projection, and filtered back-projection are a few of the techniques used to form reconstructed data from view data obtained from an X-ray scanning device.
It should be appreciated that the view data may be of any dimensionality. For example, the view data may be two dimensional (2D) representing a cross-section or slice of an object being scanned. The 2D view data may be reconstructed to form reconstructed data in two dimensional object space. This process may be repeated with view data obtained over successive slices of an object of interest. The reconstructed data may be stacked together to form reconstructed data in 3D (e.g., 3D voxel data I(xi, yi, zi)). In medical imaging, computed tomography (CT) images may be acquired in this manner.
Reconstructed data contains less information than the view data from which the reconstructed data was computed. The loss in information is due, at least in part, to the discrete nature of X-ray scanning (i.e., a finite number of detectors and a finite number of views) and to assumptions made during back-projection. In this respect, reconstructed data represents intensity as a discrete function of space. The term “intensity” refers generally to a magnitude, degree and/or value at some location in the data, whether it be view data or reconstructed data. To back-project view data, the scan plane (i.e., the 2D cross-section of the object being scanned) may be logically partitioned into a discrete grid of pixel regions.
The reconstruction process, when determining intensity values for each of the pixel regions, typically operates on the assumption that all structure within a pixel region has a same and single density and therefore computes an average of the density values within the corresponding region of space. This averaging blurs the reconstructed data and affects the resulting resolution. When multiple structures are sampled within a single pixel (e.g., when structure within the object is smaller than the dimension of the corresponding pixel region and/or the boundary of a structure extends partially into an adjacent pixel region), information about the structure is lost. The result is that the reconstructed data has less resolution than the view data from which it was generated. This loss of resolution may obscure and/or eliminate detail in the reconstructed data.
In conventional medical imaging, a human operator, such as a physician or diagnostician, may visually inspect reconstructed data to make an assessment, such as detection of a tumor or other pathology or to otherwise characterize the internal structures of a patient. However, this process may be difficult and time consuming. For example, it may be difficult to assess 3D biological structure by attempting to follow structure through stacked 2D reconstructed data. In particular, it may be perceptually difficult and time consuming to understand how 2D structure is related to 3D structure as it appears, changes in size and shape, and/or disappears in successive 2D slices of reconstructed data. A physician may have to mentally arrange hundreds or more 2D slices into a 3D picture of the anatomy. To further frustrate this process, when anatomical structure of interest is small, the structure may be difficult to discern or absent altogether in the reconstructed data.
Image processing techniques have been developed to automate or partially automate the task of understanding and partitioning the structure in reconstructed data. Such techniques are employed in computer aided diagnosis (CAD) to assist a physician in identifying and locating structure of interest in 2D or 3D reconstructed data. CAD techniques often involve segmenting reconstructed data into groups of related pixels (in 2D) or voxels (in 3D) and identifying the various groups of voxels, for example, as those comprising a tumor or a vessel or some other structure of interest. However, segmentation on reconstructed data has proven difficult, especially with respect to relatively small or less salient structure in the reconstructed data.
Many segmentation techniques rely, in part, on one or more filtering operations. Filtering processes involve comparing reconstructed data with a numerical operator (i.e., the filter) to examine properties of the reconstructed data. For example, filters may be applied to reconstructed data to examine higher order properties of the data, such as first derivative and second derivative information. The higher order information often reveals characteristics of the reconstructed data that suggest how the data should be segmented, such as edge features that may demarcate boundaries between structures or ridge features that identify properties of a particular structure of interest. Filters may be designed to respond, emphasize or otherwise identify any number of properties, characteristics and/or features in the reconstructed data.
Filtering may be achieved by applying a function to the reconstructed data. In particular, a filter may comprise a function or discrete collection of numbers over the domain of the filter, referred to as the filter kernel. The filter may be superimposed on the reconstructed data and the underlying data convolved with the filter kernel to generate a value at the location (e.g., the center of the kernel) at which the kernel was applied. The filter may then be applied to the reconstructed data at a new location, and convolved with the reconstructed data to generate another value. This process may be repeated over all the reconstructed data or desired portion of the reconstructed data to generate new data having the filter output at each location as the intensity. Alternatively, the filter outputs may be used to modify, label or otherwise augment the reconstructed data being operated on.
A filter may be n-dimensional. That is, the domain of the filter may be a continuous or discrete function over any number of dimensions. For example, 2D filters and 3D filters may be applied to 2D and 3D reconstructed data to detect and/or identify properties of the data that facilitate locating structure of interest or otherwise facilitating the segmentation of the reconstructed data. A vast array of filters are known in the art such as Gaussian filters, derivative Gaussian filters, Hessian filters, edge detectors such as difference filters like the Sobel and Canny operators, and numerous other filters specially designed to perform a specific image processing task.
Reconstructed data from view data obtained from conventional X-ray scanning devices may be limited in resolution due, in part, to the lossy reconstruction process. For example, reconstructed data from some conventional X-ray scanning devices may be limited to a resolution of approximately 500 microns. As a result, conventional imaging techniques may be unable to capture structure having dimensions smaller than 500 microns. That is, variation in the density distribution of these small structures cannot be resolved by conventional reconstruction. Micro-computer tomography (microCT) can produce view data of small objects at resolutions that are an order of magnitude greater than conventional X-ray scanning devices. However, microCT cannot image large objects such as a human patient and therefore is unavailable for in situ and generally non-invasive scanning of the human anatomy.
The ability of filtering techniques to discriminate patterns, characteristics and/or properties in reconstructed data is limited to the resolution of the reconstructed data. Blurring and loss of information due to the reconstruction process frustrates a filter's ability to identify, distinguish and/or locate characteristics or properties in reconstructed data at high resolutions. Accordingly, conventional filtering techniques on reconstructed data have been ineffective at identifying and/or detecting the presence of relatively small structure that may be of interest.